Oliver Martínez López: ENV170 Final Project

Code for StoryMaps Presentation “Environmental Cost of Cryptocurrencies: Who is Responsible?”

Load R packages for data imports, statistical analyses, and data visualizations

library(ggplot2) 
library(tidyverse)
library(readr)
library(readxl)
library(rlang)
library(htmlwidgets) 
library(plotly)
setwd("~/Library/CloudStorage/Box-Box/FALL2021/ENV170/FINALPROJECT")

Importing data on CO2 emissions, Terawatt hours consumed, and Bitcoin trading volume

MASTER_DATASET <- read_excel("DATASETS/MASTER/MASTER_DATASET.xlsx", 
    sheet = "Master", col_types = c("text", 
        "numeric", "numeric", "numeric", 
        "numeric"))
MASTER_DATASET

## # A tibble: 19 × 5
##    ISO   Terawatt_Hours Carbon_Emissions Trading_Volume GINI_2020
##    <chr>          <dbl>            <dbl>          <dbl>     <dbl>
##  1 ARE             345.            20.8           47.8       42.4
##  2 AUS             248.            15.5           54.8       34.7
##  3 BRA             553.             2.04          25.2       53.1
##  4 CAN             572.            15.5           65.6       33.3
##  5 CHN            6880.             7.41         198.        NA  
##  6 DEU             559.             8.56          NA         32.4
##  7 ESP             256.             5.52          NA         34.8
##  8 EU             1601.            24.1          204.        NA  
##  9 FRA             474.             4.62          NA         32.4
## 10 IDN             275.             2.18           8.84      38.3
## 11 IND            1309.             1.80          63.7       35.9
## 12 ITA             312.             5.38          NA         36.2
## 13 JPN             955.             8.74           6.66      32.9
## 14 KOR             563.            12.2           NA         NA  
## 15 MEX             285              3.74          23.5       45.1
## 16 RUS             997.            11.1          421.        36.6
## 17 TUR             272              5.02           5         42.2
## 18 USA            4194.            15.2         1524.        42.0
## 19 ZAF             229.             7.50           9.92      NA

Check to see if data is normal or non-normal

Using shapiro test to determine the kind of distribution. If P-val is greater than alpha = 0.05, then it is normal.

shapiro.test(MASTER_DATASET$Terawatt_Hours) #p-value = 1.561e-06

## 
##  Shapiro-Wilk normality test
## 
## data:  MASTER_DATASET$Terawatt_Hours
## W = 0.55371, p-value = 1.561e-06

shapiro.test(MASTER_DATASET$Carbon_Emissions) #p-value = 0.08199

## 
##  Shapiro-Wilk normality test
## 
## data:  MASTER_DATASET$Carbon_Emissions
## W = 0.91242, p-value = 0.08199

shapiro.test(MASTER_DATASET$Trading_Volume) #p-value = 6.233e-06

## 
##  Shapiro-Wilk normality test
## 
## data:  MASTER_DATASET$Trading_Volume
## W = 0.50281, p-value = 6.233e-06

#Conclusion: None are normally distributed data

Run correlation tests to determine significance

cor.test(x = MASTER_DATASET$Trading_Volume, y = MASTER_DATASET$Carbon_Emissions,  method = "spearman") #0.4461538

## 
##  Spearman's rank correlation rho
## 
## data:  MASTER_DATASET$Trading_Volume and MASTER_DATASET$Carbon_Emissions
## S = 252, p-value = 0.1119
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.4461538

cor.test(x = MASTER_DATASET$Terawatt_Hours, y = MASTER_DATASET$Carbon_Emissions,  method = "spearman") #0.245614 

## 
##  Spearman's rank correlation rho
## 
## data:  MASTER_DATASET$Terawatt_Hours and MASTER_DATASET$Carbon_Emissions
## S = 860, p-value = 0.3094
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##      rho 
## 0.245614

#Conclusion: limited data so not statistically significant

Run histograms to better visualize data

ggplot(MASTER_DATASET, aes(Terawatt_Hours)) + geom_histogram(bins=10, color= "black", fill="mediumvioletred") + geom_vline(aes(xintercept=mean(Terawatt_Hours)),
            color="skyblue3", linetype="dashed", size=1) + labs(x="Terawatt per Hour Consumption")

ggplot(MASTER_DATASET, aes(Carbon_Emissions)) + geom_histogram(bins=10, color= "black", fill="mediumseagreen") + geom_vline(aes(xintercept=mean(Carbon_Emissions)),
            color="skyblue3", linetype="dashed", size=1) + labs(x="Carbon Emissions Data")

ggplot(MASTER_DATASET, aes(Trading_Volume)) + geom_histogram(bins=10, color= "black", fill="dodgerblue") + geom_vline(aes(xintercept=mean(Trading_Volume)),
            color="skyblue3", linetype="dashed", size=1) + 
  labs(x="Trading Volume of BTC")

Now, we plot the correlation between the variables

First, we plot CO2 emissions v. electricity consumption in Terawatt per hour

HK <-   
  ggplot(MASTER_DATASET, aes(x = Terawatt_Hours, y=Carbon_Emissions, label=(ISO))) +
  geom_point(colour ="mediumvioletred", size = 2.5) +
  labs(x = "Electricity Consumption in Terawatt Hours in 2019", y = "CO2 emissions (metric tons per capita) in 2018") 

HKP<- HK + geom_text(size=2.1, hjust = 0.80, vjust=2, nudge_x = 0.05, check_overlap = FALSE)
HKP

#Using HKP for final project

Now, we plot Bitcoin trading volume v. electricity consumption in Terawatt per hour

BB <-   
  ggplot(MASTER_DATASET, aes(x = Trading_Volume, y=Terawatt_Hours, label=(ISO))) +
  geom_point()+
  labs(x = "Bitcoin Trading Volume on Online Exchanges by Country 2020. (in millions of U.S. dollars)", y = "Electricity Consumption in Terawatt Hours in 2019") +
   geom_vline(
    aes(xintercept=mean(Trading_Volume)),
            color="blue", linetype="dashed", size=1) + 
  geom_hline(
    aes(yintercept=mean(Terawatt_Hours)), 
            color="blue", linetype="dashed", size=1)

BBP<- BB + geom_text(size=2.1, hjust = 0.80, vjust=2, nudge_x = 0.05, check_overlap = FALSE)
BBP

Now, we plot CO2 Emissions v. BTC trading volume

GOW <-   
  ggplot(MASTER_DATASET, aes(x = Trading_Volume, y=Carbon_Emissions, label=(ISO))) +
  geom_point(colour = "dodgerblue", size = 2.5) +
  labs(x = "Bitcoin Trading Volume on Online Exchanges by Country 2020. (in millions of U.S. dollars)", y = "C02 emissions (metric tons per capita) in 2018")

GOWP<- GOW + geom_text(size=2.1, hjust = 0.80, vjust=2, nudge_x = 0.05, check_overlap = TRUE)
GOWP

#Using GOWP for final project

Import data on CO2 Emissions only

CO2_4 <- read_csv("DATASETS/CO2_Emissions/CO24CLEAN.csv")
CO2_4

## # A tibble: 266 × 63
##     ...1 `Country Name`       `1960`   `1961`   `1962`   `1963`   `1964`  `1965`
##    <dbl> <chr>                 <dbl>    <dbl>    <dbl>    <dbl>    <dbl>   <dbl>
##  1     1 Aruba              205.     209.     226.     215.     208.     185.   
##  2     2 Africa Eastern an…   0.906    0.922    0.931    0.941    0.996    1.05 
##  3     3 Afghanistan          0.0461   0.0536   0.0737   0.0742   0.0862   0.101
##  4     4 Africa Western an…   0.0909   0.0953   0.0966   0.112    0.133    0.185
##  5     5 Angola               0.101    0.0822   0.211    0.203    0.214    0.206
##  6     6 Albania              1.26     1.37     1.44     1.18     1.11     1.17 
##  7     7 Andorra             NA       NA       NA       NA       NA       NA    
##  8     8 Arab World           0.609    0.663    0.727    0.853    0.972    1.14 
##  9     9 United Arab Emira…   0.119    0.109    0.164    0.176    0.133    0.147
## 10    10 Argentina            2.38     2.46     2.54     2.33     2.55     2.66 
## # … with 256 more rows, and 55 more variables: 1966 <dbl>, 1967 <dbl>,
## #   1968 <dbl>, 1969 <dbl>, 1970 <dbl>, 1971 <dbl>, 1972 <dbl>, 1973 <dbl>,
## #   1974 <dbl>, 1975 <dbl>, 1976 <dbl>, 1977 <dbl>, 1978 <dbl>, 1979 <dbl>,
## #   1980 <dbl>, 1981 <dbl>, 1982 <dbl>, 1983 <dbl>, 1984 <dbl>, 1985 <dbl>,
## #   1986 <dbl>, 1987 <dbl>, 1988 <dbl>, 1989 <dbl>, 1990 <dbl>, 1991 <dbl>,
## #   1992 <dbl>, 1993 <dbl>, 1994 <dbl>, 1995 <dbl>, 1996 <dbl>, 1997 <dbl>,
## #   1998 <dbl>, 1999 <dbl>, 2000 <dbl>, 2001 <dbl>, 2002 <dbl>, 2003 <dbl>, …

Extract data from 2009 to 2018 to match timeframe Bitcoin has been in ciculation

CO2_5 <- select(CO2_4, -c(2:50))
CO2_52 <- select(CO2_5, -c(13:14))

Now, get only data for the United States from 2008 to 2018

newdf <- CO2_52 %>% slice(252,) #US row is 252
newdf2<-t(newdf)
newdf2<-as.data.frame(newdf2)
newdf3 <-newdf2 %>% slice(-1,)

colnames(newdf3)[1] <- "CO2 Emissions"
newdf3

##      CO2 Emissions
## 2008      18.29481
## 2009      16.81887
## 2010      17.43420
## 2011      16.59940
## 2012      15.77114
## 2013      16.10296
## 2014      16.02638
## 2015      15.53534
## 2016      15.13175
## 2017      14.80588
## 2018      15.24087

#Make row names into first column
d <- newdf3
Years <- rownames(d)
rownames(d) <- NULL
newdf4 <- cbind(Years,d)

newdf4 #Make character as numeric

##    Years CO2 Emissions
## 1   2008      18.29481
## 2   2009      16.81887
## 3   2010      17.43420
## 4   2011      16.59940
## 5   2012      15.77114
## 6   2013      16.10296
## 7   2014      16.02638
## 8   2015      15.53534
## 9   2016      15.13175
## 10  2017      14.80588
## 11  2018      15.24087

newdf4$Years<- as.character(newdf4$Years)
newdf4$`CO2 Emissions`<- as.numeric(newdf4$`CO2 Emissions`)
newdf4

##    Years CO2 Emissions
## 1   2008      18.29481
## 2   2009      16.81887
## 3   2010      17.43420
## 4   2011      16.59940
## 5   2012      15.77114
## 6   2013      16.10296
## 7   2014      16.02638
## 8   2015      15.53534
## 9   2016      15.13175
## 10  2017      14.80588
## 11  2018      15.24087

Plot CO2 emissions in the US for the years 2008 to 2018

#Plot data
ggplot(data = newdf4) +
  geom_point(aes(x = Years, y = `CO2 Emissions`)) 

p <- ggplot(data = newdf4) + geom_point(aes(x = Years, y = `CO2 Emissions`)) +
  theme(axis.text.x = element_text(angle = 0, vjust = 0.5)) + 
  geom_line(aes(x = Years, y = `CO2 Emissions`, group=1)) 
p

Now, add layers to this plot

p + xlab("Years") + ylab("Carbon Emissions (in millions)") +
  theme(plot.title = element_text(hjust = 0.5))+
  theme_bw()

p2 <- p + xlab("Years") + ylab("Carbon Emissions (in millions)") +
  theme(plot.title = element_text(hjust = 0.5))+
  theme_bw()
p2

ggplotly(p2)

USCarbonEmissions<- plotly::ggplotly(p2)
htmlwidgets::saveWidget(USCarbonEmissions, "USCarbonEmissions.html")

Import new dataset using Bitcoin data

Import yearly data on BTC in circulation for the years 2009-2018.

BTC <- read_excel("~/Library/CloudStorage/Box-Box/FALL2021/ENV170/FINALPROJECT/DATASETS/BTC/BTCincirculation.xlsx", 
                             sheet = "BTC")

colnames(BTC)[colnames(BTC) == "Year"] <- "Years"

BTC$Years <- as.character(BTC$Years)

Plot BTC data over time

p3 <- ggplot(data = BTC) + 
  geom_line(aes(x = Years, y = BTC, group = 1)) +
  geom_point(aes(x = Years, y = BTC))
p3

p4golden <- p3 + xlab("Years") + ylab("Millions of Bitcoins in existence") +
  theme(plot.title = element_text(hjust = 0.5))+
  theme_bw()
p4golden

#Use plotly::ggplotly() function to make an interactive plot 
ggplotly(p4golden)

BitcoinsCirculation<- plotly::ggplotly(p4golden)
htmlwidgets::saveWidget(BitcoinsCirculation, "BitcoinsCirculation.html")

Merge Datasets. R will merge by “Years” by default

us_btc_co2<-left_join(BTC, newdf4)

## Joining, by = "Years"

write.csv(us_btc_co2, "US BTC and CO2 Emissions.csv")

Final reflections:

1. Rank your visualizations from 1-5 (or more) in the order in which you would like them to be evaluated

1. CO2 Emissions v. Electricity Consumption in Terawatt per hours
2. CO2 Emissions v. BTC Trading Volume
3. CO2 Emissions since 2008 to 2018 (plotly)
4. BTC millions in circulation (plotly)
5. Terawatt per hour consumption histogram
6. Carbon emissions histogram
7. BTC trading volume histogram

2. Which visualizations, if any, were you most pleased with creating and

why? I was most pleased with my correlation visualizations between CO2 emissions against electricity consumption and BTC trading volume because they were by far the most complex out of any other visualization I worked on. I decided to label each data point by country ISO (3 letter code word) because it was easier for the reader to know where world economies sit on my measures.

I was also very proud of creating my plotly charts because they required me to use an outsite repository so I could include them into my presentation. It made my charts more interactive and user-friendly.

3. Did you have any issues accessing the data for your project? How did you resolve these?

I didn’t have any problem accessing data for this project because Bitcoin data is widely accessible from countries who use it the most. However, I would have liked to include more data from different countries to have better correlation results and to show where other world economies are with regards to my measures. I found it easier to access information from countries with easier access to information (i.e. the United States and many other European countries)

4. How did your project topic evolve from what you first envisioned, and why?

I envisioned having a side-by-side comparison with how much energy cryptocurrrencies consume vis-a-vis cash, using Bitcoin and the USD respectively. However, this would have required a much more in-depth analysis of two major currencies.

I ultimately decided to stay on the issue of cryptocurrency mining so as to not deviate from my main point——large world economies are the responsible agents for emitting greenhouse gas emissions.

5. Did you have issues related to any analyses that you performed, the timeline of the project, your computer or other tools necessary, etc? How did you resolve or workaround these issues?

At first I was not sure what kind of correlation test I had to run because I was not sure whether my data was normally distributed or not. I ended up running shapiro.test() to more accurately describe the restults I was seeing with my world economies data point. Therefore, I ran a spearman correlation on my graphs to more accurately represent my correlation values.

I predicted that my correlation tests would not yield significant results because of the limited data I was working with. Despite this, I thought it would be useful to still include my results to pursue a narrative that focuses more on which economies are largely responsible for large electricity consumption and greenhouse gas emissions.

When running my graophs that involved changes in BTC circulation or CO2 emissions in the U.S. over time, I came across hard-to-read data. For instance, I realized that data on BTC circulation was clearly laid out across all the months for 2017, 2018, and 2019, whereas data from 2009 and 2010 only had datapoints on a few months. Given these inconsistencies, I chose to stick with one month for all years–the months of October. Because data for the months of October were available since 2009 to 2018, I decided to show those data points only so as not to confuse my readers.

I ran into minor issues with my computer shutting down randomly, so I made use of the Tisch Data Lab computers to work on my code. With the help of my housemates Laura Hurvey and Uyen Chu I was able to work with the rest of my code just fine.

6. If you had more time, what would you change (e.g., things you might add or remove)?

I would have liked to draw comparisons between how much energy a single unit of BTC costs to the environment vis-a-vis the US dollar. Making this side-by-side comparison was at the heart of my final project since the beginning, but due to time constraints I could not pursue this narrative.

I had been working with my work supervisor from the Fletcher School’s Institute for Business in the Global Context to delve into this topic since the beginning of the semester. Having worked as a research assistant at this office, I had a better idea as to how I wanted to tackle this project. While I received my work supervisor’s feedback on my analysis, I would have liked to explore my data a bit further.

7. Are you happy with the final results of your project?

Yes! Learning R was a big learning curve for me and I feel much more comfortable with the statistical and visualization aspects of my R repertoire. This project was a big step in my coding and statistical knowledge and my results are reflective of how much care and thought I put into my work. Despite not finding statistically significant correlations with my data, I gained a better insight in the field of data analysis and visualizations.

I am happy with my final project and I know I will come back to this as the foundation that got me started with my R knowledge and understanding of statistics.

8. May I use your project and/or visualizations as an example for later

Please do!

9. What advice would you give to future students taking this course?

When I found myself in a challenging situation in class, I relied a lot on my housemates for a better understanding of the material. When I was not able to make it to office hours, they offered help in the best way possible.

Forming a study group goes a long way in really getting on top of class. I often found myself helping my “next door neighbor” sitting next to me, and that made a big difference in how much better they could follow along with the course material. The same goes for when I needed help.

I realized asking for help could solve my issues in a much shorter time than it would have taken me if I wanted to figure things out all by myself.